1. Field of the Invention
The present invention realtes to a neural network with learning function, a method of learning therefor, a method of performing integrated processing of a plurality of information such as time-series information, and a neural network system therefor.
2. Description of the Related Art
Conventionally, as disclosed in "Parallel Distributed Processing I and II", (1986), there have been a method of learning and storing one-to-one correspondence relationship between data. The conventional method fails to take into full consideration the characteristics of a neural network which is that a desired task can be satisfactorily executed by mixing and processing features of input data different in natural from each other, for example. Therefore, processing such as pattern recognizing, as well as processing of data changing with time such as time series data cannot be fully processed.
Development of a neural network system handling time series data appears to be made considerably lower than that of a static processing system for processing a static signal or image. A multilayer neural network is designed to learn and store input data and mapping relation of the data with corresponding data which is generally encoded. For this reason, there is no room for time as a data element to be incorporated, so that when dynamics such as time series data are an object of processing, a new network configuration is required. The neural network already proposed can of course process time-series data, if data within a given (time) interval can be regarded as a static block of patterns. However, in this method, the time correlation important for the time series data is entirely ignored. Thus, none of the conventional neural network systems has necessarily get a success in processing time series data. Even the processing of audio data in which the time relation is important is grasped only as a problem of storage of mapping of static data. No reference is found in which a neural network system is designed from the viewpoint of learning and storing of the time correlation which is essential for time series data.